Developed DICOM standard schema with DSpace

Purpose This paper aims to present a novel DSpace-based medical image repository system planned explicitly for storing and retrieving clinical images using digital imaging and communication in medicine (DICOM) metadata standards. DSpace institutional repository software is widely used in an academic environment for accessing and mainly storing text-related files. DICOM images are particular types of images embedded with much system-generated metadata and organised using DICOM metadata standards. Design/methodology/approach The present paper talks about institutional repository software (DSpace) in archiving DICOM images. In the current study, the authors have tried to integrate the DICOM metadata standard with DSpace, which was compatible with Dublin Core (DC) and open archives initiative – protocol for metadata harvesting (OAI-PMH). After combining the DICOM standard with DSpace and the repository tested with a sample of 5,000 images, the retrieval results using various DICOM tags was very satisfactory. This study paves for the use of open source software (OSS) in storing and retrieving medical images. Findings The author has provided the DSpace software to recognised DICOM (.dcm) files in the first stage. In the second stage, a patch was developed to identify the DICOM metadata standard in Dspace, which has inbuilt DC metadata standards. Finally, in the third stage, retrieval efficiency was tested with a 5,000 .dcm image using the DICOM tag and the results were very fruitful. Research limitations/implications A major limitation of this study was the size of the data (5,000 DICOM images) with which the authors have tested the system. The system scalability has to be tested on various fronts like on cloud and local servers with different configurations, for which a separate study has to be done. Practical implications Once this system is in place, DICOM users can stock, retrieve and access the image from the Web platform. Furthermore, this proposed repository will be the warehouse of various DICOM images with reasonable storage costs. Originality/value In addition to exploring the opportunities of free open source software (FOSS) implementation in medical science, this study includes issues related to the performance of an open-source repository for retrieving and preserving medical images. It created and developed Open Source DICOM Medical Image Library with DICOM metadata standard with the help of DSpace. Thus, the study will generate value for library professionals and medical professionals and FOSS vendors to understand the medical market in the context of FOSS.

A DICOM dataset for evaluation of medical image de-identification

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.

The Role of DICOM in Artificial Intelligence for Skin Disease

There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology. Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging. Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging. DICOM is a continually evolving standard. There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard. The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (e.g., image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology. DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts. DICOM can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation. This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols. DICOM can address several important technological and workflow challenges for the implementation of AI. However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology.

DICOM’s Standardization in Histo-Pathology

Background: The Digital Imaging and Communications in Medicine (DICOM) standard helps to represent, store, and to exchange healthcare images associated with its data. DICOM develops over time and is continuously adapted to match the rigors of new clinical demands and technologies. An uphill battle in this regard is to conciliate new software programs with legacy systems. Methods: This work discusses the essential aspects of the standard and assesses its capabilities and limitations in a multisite, multivendor healthcare system aiming at Whole Slicing Image (WSI) procedures. Selected relevant DICOM attributes help to develop and organize WSI applications that extract and handle image data, integrated patient records, and metadata. DICOM must also interface with proprietary file formats, clinical metadata and from different laboratory information systems. Standard DICOM validation tools to measure encoding, storing, querying and retrieval of medical data can verify the generated DICOM files over the web. Results: This work investigates the current regulations and recommendations for the use of DICOM with WSI data. They rely mostly on the EU guidelines that help envision future needs and extensions based on new examination modalities like concurrent use of WSI with in-vitro imaging and 3D WSI. Conclusion: A DICOM file format and communication protocol for pathology has been defined. However, adoption by vendors and in the field is pending. DICOM allows efficient access and prompt availability of WSI data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology. In the future, the DICOM standard will have to address several issues due to the way samples are gathered and encompassing new imaging technologies.

DICOM Format and Protocol Standardization—A Core Requirement for Digital Pathology Success

As the use of digital techniques in toxicologic pathology expands, challenges of scalability and interoperability come to the fore. Proprietary formats and closed single-vendor platforms prevail but depend on the availability and maintenance of multiformat conversion libraries. Expedient for small deployments, this is not sustainable at an industrial scale. Primarily known as a standard for radiology, the Digital Imaging and Communications in Medicine (DICOM) standard has been evolving to support other specialties since its inception, to become the single ubiquitous standard throughout medical imaging. The adoption of DICOM for whole slide imaging (WSI) has been sluggish. Prospects for widespread commercially viable clinical use of digital pathology change the incentives. Connectathons using DICOM have demonstrated its feasibility for WSI and virtual microscopy. Adoption of DICOM for digital and computational pathology will allow the reuse of enterprise-wide infrastructure for storage, security, and business continuity. The DICOM embedded metadata allows detached files to remain useful. Bright-field and multichannel fluorescence, Z-stacks, cytology, and sparse and fully tiled encoding are supported. External terminologies and standard compression schemes are supported. Color consistency is defined using International Color Consortium profiles. The DICOM files can be dual personality Tagged Image File Format (TIFF) for legacy support. Annotations for computational pathology results can be encoded.